Abstract: A tire wear state estimation system estimates forces and sliding velocity generated in a tire contact patch, determines frictional energy from the tire force and sliding velocity, and generates an estimate of tire wear state based upon the frictional work done by the tire. A tire wear estimate, pursuant to the system and methodology, is made by determining the amount of frictional work performed by the tire through the integrated use of tire-mounted, GPS sourced, and vehicle-mounted sensor information.

Abstract: A system monitors and controls pressure of vehicle parts. The system consists of: a fleet of vehicles, each vehicle of the fleet having a cab and a trailer; a controller maintaining a wireless server and database for the fleet of vehicles; a first cellular device transmitting data to and from an electronic control unit of the cab of each vehicle and the controller; and a second cellular device transmitting data to and from an electronic control unit of the trailer of each vehicle and the controller. The first cellular device and the second cellular device each utilize a publicly accessible cellular network for communication with the controller.

Abstract: A system for predicting tire lift-off propensity of a vehicle tire includes a vehicle tire-affixed tire-identification device for providing a tire-specific identification, multiple tire-affixed sensors mounted to the tire measuring tire-specific parameters and generating tire-specific parameter information, one or more vehicle-affixed sensor(s) mounted to the vehicle to measure vehicle speed and a lift-off propensity estimator generating a lift-off propensity for the vehicle tire from a database containing experimentally-derived, tire-ID specific, lift-off propensities correlated with measured tire-specific parameter information and vehicle speeds.

Abstract: A system monitors and controls pressure of vehicle parts. The system consists of: a fleet of vehicles, each vehicle of the fleet having a cab and a trailer; a controller maintaining a wireless server and database for the fleet of vehicles; a first cellular device transmitting data to and from an electronic control unit of the cab of each vehicle and the controller; and a second cellular device transmitting data to and from an electronic control unit of the trailer of each vehicle and the controller. The first cellular device and the second cellular device each utilize a publicly accessible cellular network for communication with the controller.

Abstract: A system and method of estimating a load bearing on a vehicle tire includes an inflation pressure measuring sensor for measuring an inflation pressure level within a tire cavity; a contact patch length sensor measuring the rolling contact patch length of the tire; a tire rolling speed sensor measuring a rolling speed of the tire; and a tire wear state estimation calculator estimating a tire wear state of a tread of the tire. An artificial neural network estimates a tire load from the contact patch length, the inflation pressure level, and the tire rolling speed by utilizing as a compensation factor input the estimated tire wear state.

Abstract: A system monitors and controls pressure of vehicle parts. The system consists of a fleet of vehicles, each vehicle of the fleet having a cab and a trailer; a controller maintaining a wireless server and database for the fleet of vehicles; and a cellular device transmitting data to and from an electronic control unit of the cab of each vehicle and the controller. Each trailer has an electronic control unit transmitting data to/from the electronic control unit of each corresponding cab.

Abstract: A system monitors wear of a vehicle part. The system includes a piezoelectric disk electrically connected to a radio frequency circuit for receiving electrical current from the piezoelectric disk and an antenna for actively transmitting a signal from the radio frequency circuit to a microprocessor such that, when the piezoelectric disk contacts a surface through wear, the radio frequency circuit actively generates a signal received by the microprocessor.

Abstract: A dynamic load estimation system and method is provided, the system including a tire supporting a vehicle; a vehicle-mounted acceleration sensor for determining a vehicle lateral acceleration and a vehicle longitudinal acceleration; a roll angle calculating model for determining a vehicle roll angle; a roll rate calculating model for determining a vehicle roll rate; a static normal load calculation model for calculating a measured static normal load; and a dynamic tire load estimation model for calculating an estimated dynamic load on the tire from the measured static normal load, the vehicle roll angle, the vehicle roll rate, the vehicle lateral acceleration and the vehicle longitudinal acceleration.

Abstract: A slip angle estimation includes a tire having one or more first and second strain sensor(s) affixed to opposite respective first and second tire sidewalls. The sensors measure a tire strain in their respective sidewalls and generate a sidewall strain signal indicative of strain present within the tire sidewalls. A slip angle estimation is made by estimating the difference in the signal slope of the sensors in the opposite sidewalls. A load estimation is further made for the tire from the inner and outer sidewall strain signals and the load estimation is used in the slip angle estimation.

Abstract: A dynamic slip angle estimation system and method uses measured vehicle acceleration and yaw rate parameters in estimating a tire slip angle. From load sensor(s) mounted to the vehicle tire, a tire static load estimation is made and a tire slip angle is calculated at low frequency. The vehicle center of gravity longitudinal position and yaw moment of inertia is estimated from the static load on the vehicle tires. An observer calculates tire axle forces based on the vehicle acceleration and yaw rate. From the tire axle force estimations, the vehicle moment of inertia and vehicle center of gravity longitudinal position estimate and a low frequency direct measurement of the tire slip angle, a dynamic tire slip angle calculation is made.

Abstract: A dynamic load estimation system is provided including: a vehicle load bearing tire; at least one tire sensor mounted to the tire, the sensor operable to measure a tire deformation of the one tire and generate a raw load-indicating signal conveying measured deformation data; road roughness estimation means for determining a road roughness estimation; filtering means for filtering the measured deformation data by the road roughness estimation; and load estimation means for estimating an estimated load on the one tire from filtered measured deformation data. A road profile estimate is fused with the static load estimate in order to obtain an instantaneous tire load estimate.

Abstract: A dynamic slip angle estimation system and method uses measured vehicle acceleration and yaw rate parameters in estimating a tire slip angle. From load sensor(s) mounted to the vehicle tire, a tire static load estimation is made and a tire slip angle is calculated at low frequency. The vehicle center of gravity longitudinal position and yaw moment of inertia is estimated from the static load on the vehicle tires. An observer calculates tire axle forces based on the vehicle acceleration and yaw rate. From the tire axle force estimations, the vehicle moment of inertia and vehicle center of gravity longitudinal position estimate and a low frequency direct measurement of the tire slip angle, a dynamic tire slip angle calculation is made.

Abstract: A dynamic load estimation system and method is provided, the system including a tire supporting a vehicle; a vehicle-mounted acceleration sensor for determining a vehicle lateral acceleration and a vehicle longitudinal acceleration; a roll angle calculating model for determining a vehicle roll angle; a roll rate calculating model for determining a vehicle roll rate; a static normal load calculation model for calculating a measured static normal load; and a dynamic tire load estimation model for calculating an estimated dynamic load on the tire from the measured static normal load, the vehicle roll angle, the vehicle roll rate, the vehicle lateral acceleration and the vehicle longitudinal acceleration.

Abstract: A dynamic load estimation system is provided including: a vehicle load bearing tire; at least one tire sensor mounted to the tire, the sensor operable to measure a tire deformation of the one tire and generate a raw load-indicating signal conveying measured deformation data; road roughness estimation means for determining a road roughness estimation; filtering means for filtering the measured deformation data by the road roughness estimation; and load estimation means for estimating an estimated load on the one tire from filtered measured deformation data.

Abstract: An autonomous, plug-in and sacrificial wear sensing system including at least one wearable sensor, autonomous energy supply and means for remote radio-frequency communication. The system includes a tubular tread wear indicator body embedded within a tire tread element. The indicator body is centered within the tread element and is shaped in the form of a tube. The sensor element operably changes in at least one electromagnetically measurable physical parameter as the tread element wears radially inward. The system provides a full tread wear system including the algorithm for signal and data processing, buffering and decision making routines to remotely provide vehicle driver or owner with current tire wear status, tire type—vehicle—season match and with real-time estimation of remaining tire life.

Abstract: A slip angle estimation includes a tire having one or more first and second strain sensor(s) affixed to opposite respective first and second tire sidewalls. The sensors measure a tire strain in their respective sidewalls and generate a sidewall strain signal indicative of strain present within the tire sidewalls. A slip angle estimation is made by estimating the difference in the signal slope of the sensors in the opposite sidewalls. A load estimation is further made for the tire from the inner and outer sidewall strain signals and the load estimation is used in the slip angle estimation.

Abstract: The present invention is directed to a method of reducing noise transmission in a pneumatic tire and wheel assembly comprising an inflation cavity, the method comprising the step of adding to the inflation cavity from about 0.1 to about 1 volume percent, based on the volume of the inflation cavity, of a repeatably foamable liquid.

Abstract: The present invention is directed to a method of reducing noise transmission in a pneumatic tire and wheel assembly comprising an inflation cavity, the method comprising the step of adding to the inflation cavity from about 0.1 to about 1 volume percent, based on the volume of the inflation cavity, of a repeatably foamable liquid.

Abstract: The invention relates to methods and apparatus suitable for executing a service or application at a client peer or client side, having a client specific device or client specific platform, with a reconfigurable architecture, said service or application being provided from a service peer or a service side. In a first aspect of the invention, the method comprises transmitting to the client peer from the server peer an abstract bytecode. The abstract bytecode is generated at the service peer by performing a compilation of an application. The abstract bytecode includes hardware bytecode and software bytecode. At the client peer, the abstract bytecode is transformed into native bytecode for the client specific device.

Abstract: A method of processing received L1 and L2 spread spectrum signals is disclosed. In one embodiment, the method comprises i) locally generating replicas of a known P-code, wherein each of the received signals includes a unique frequency carrier with the known pseudo-random P-code and an unknown code modulated thereon, ii) making the code replicas available at different relative phases, iii) demodulating the received L1 and L2 signals with replicas of the P-code, iv) repetitively and separately integrating the demodulated L1 and L2 signals over time periods related to the unknown code, and v) correlating an integration result for one of the L1 and L2 signals with an integration result for the other of the L1 and L2 signals.